Considering security issues in ML algorithms
Someone is going to break into your ML application, even if you keep it behind firewalls on a local network. The following sections will help you understand the security issues that lead to breaches when using adversarial ML techniques.
Considering the necessity for investment and change
Because of the time and resource investment in ML models, organizations are often less than thrilled about having to incorporate new research into the model. However, as with any other software, updates of ML models and the underlying libraries represent an organization’s investment in the constant war with hackers. In addition, an organization needs to remain aware of the latest threats and modify models to combat them. All these requirements may mean that your application never feels quite finished – you may just complete one update, only to have to start on another.
Defining attacker motivations
An organization can use any number...
